The Role of Electroencephalography in Advancing Sleep Research
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Abstract
Electroencephalography (EEG) is fundamental in sleep research, providing critical insights into cerebral activity and significantly contributing to the diagnosis of sleep disorders. This study examines recent progress in EEG-based sleep research, emphasizing cutting-edge methods for sleep staging and disease identification. The amalgamation of machine learning and deep learning methodologies, encompassing hybrid models such as CNN-LSTM, has markedly improved the precision of sleep stage categorization and automated analysis. Enhancements in signal quality and dependability, especially by improvements in artifact removal methods like wavelet-enhanced independent component analysis (ICA), have further advanced these developments. The implementation of multimodal strategies, wearable EEG technology, and AI-enhanced systems has broadened the sphere of sleep monitoring beyond clinical environments, rendering it more accessible and individualized. This article examines the use of EEG in detecting sleep disorders, including insomnia, obstructive sleep apnea, and narcolepsy, by identifying biomarkers and abnormalities in sleep architecture. Emerging research underlines the promise of clinical EEG, marking it as a transformational tool for both study and therapy. Nonetheless, obstacles persist in domains such as noise reduction, biomarker standardization, and scalability. Future directions include merging EEG with imaging modalities like fMRI, developing wearable technology, and employing advanced AI for individualized sleep health management. In particular, EEG is highlighted as a transformational and promising tool for promoting sleep medicine through novel, accessible, and effective solutions.
Manuscript received: 5 Jan 2025 | Revised: 10 Feb 2025 | Accepted: 18 Feb 2025 | Published: 31 Mar 2025
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References
Y. Abichou, S. Chaabene, and L. Chaari, “A Sleep Monitoring Method with EEG Signals,” in Digital Health in Focus of Predictive, Preventive and Personalised Medicine, vol. 12, L. Chaari, Ed., 2020.
DOI: https://doi.org/10.1007/978-3-030-49815-3_4
K. A. I. Aboalayon, M. Faezipour, W. S. Almuhammadi, and S. Moslehpour, “Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation,” Entropy, vol. 18, no. 9, pp. 272, 2016.
DOI: https://doi.org/10.3390/e18090272
H. Almutairi, G. M. Hassan, and A. Datta, “Machine-learning-based approaches for sleep stage classification utilising a combination of physiological signals: A systematic review,” Applied Sciences, vol. 13, no. 24, pp. 13280, 2023.
DOI: https://doi.org/10.3390/app132413280
G. Amrani, A. Adadi, M. Berrada, Z. Souirti, and S. Boujraf, “EEG signal analysis using deep learning: A systematic literature review,” 2021 Fifth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1–8, 2021. DOI: https://doi.org/10.1109/ICDS53863.2021.00006
R. Behzad and A. Behzad, “The role of EEG in the diagnosis and management of patients with sleep disorders,” Journal of Behavioral and Brain Science, vol. 11, no. 10, pp. 257–266, 2021
DOI: https://doi.org/10.4236/jbbs.2021.1111019
A. Chaddad, Y. Wu, R. Kateb, and A. Bouridane, “Electroencephalography signal processing: A comprehensive review and analysis of methods and techniques,” Sensors, vol. 23, no. 14, pp. 6434, 2023.
DOI: https://doi.org/10.3390/s23146434
A. Chan, A. Cakir, D. Josephs, D. Kleinschmidt, J. Pathmanathan, and J. Donoghue, “1079 robust automated sleep staging using only EEG signals,” Sleep, vol. 47, Supplement 1, pp. A463–A464, 2024.
DOI: https://doi.org/10.1093/sleep/zsaa183.1079
L. Cheng, S. Luo, X. Yu, H. Ghayvat, H. Zhang, and Y. Zhang, “EEG-CLNet: Collaborative learning for simultaneous measurement of sleep stages and OSA events based on single EEG signal,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–10, 2023.
DOI: https://doi.org/10.1109/TIM.2023.3231568
J. H. Duyn, “EEG-fMRI methods for the study of brain networks during sleep,” Frontiers in Neurology, vol. 3, pp. 100, 2012. DOI: https://doi.org/10.3389/fneur.2012.00100
I. Daniyan, V. Balogun, A. Adeodu, and B. Oladapo, “Development and performance evaluation of a robot for lawn mowing applications,” Procedia Manufacturing, vol. 46, pp. 244–249, 2020.
DOI: https://doi.org/10.1016/j.promfg.2020.06.009
L. M. J. Fernandez and A. Luthi, “Sleep spindles: mechanisms and functions,” Physiological Reviews, vol. 100, no. 2, pp. 805–868, 2020.
DOI: https://doi.org/10.1152/physrev.00026.2019
M. Fu, Y. Wang, Z. Chen, J. Li, F. Xu, X. Liu, and F. Hou, “Deep learning in automatic sleep staging with a single channel electroencephalography,” Frontiers in Physiology, vol. 12, pp. 628502, 2021.
DOI: https://doi.org/10.3389/fphys.2021.628502
S. G., “Sleep disorder detection using EEG signals,” International Journal of Scientific Research in Engineering and Management, vol. 8, pp. 1–5, 2024.
DOI: https://doi.org/10.55041/ijsrem34611
C. E. Gonzalez, R. A. Mak-McCully, B. Q. Rosen, S. S. Cash, P. Y. Chauvel, H. Bastuji, M. Rey, and E. Halgren, “Theta bursts precede, and spindles follow, cortical and thalamic downstates in human NREM sleep,” Journal of Neuroscience, vol. 38, no. 46, pp. 9989–10001, 2018.
DOI: https://doi.org/10.1523/JNEUROSCI.2295-18.2018
A. J. Farhan, "Fabrication and Development Low Cost Dual Axis Solar Tracking System," IOP Conference Series: Materials Science and Engineering, vol. 757, no. 1, 2020.
DOI: https://doi.org/10.1088/1757-899X/757/1/012042
H. Hindarto and S. Sumarno, “Feature extraction of electroencephalography signals using fast Fourier transform,” CommIT (Communication and Information Technology) Journal, vol. 10, no. 2, pp. 49–52, 2016
DOI: https://doi.org/10.20473/commit.v10i2.49-52
I. Hussain, M. A. Hossain, R. Jany, M. A. Bari, M. Uddin, A. R. M. Kamal, Y. Ku, and J.-S. Kim, “Quantitative evaluation of EEG-biomarkers for prediction of sleep stages,” Sensors, vol. 22, no. 8, pp. 3079, 2022
DOI: https://doi.org/10.3390/s22083079
H. Korkalainen, J. Aakko, S. Nikkonen, S. Kainulainen, A. Leino, B. Duce, I. O. Afara, S. Myllymaa, J. Toyras, and T. Leppanen, “Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 7, pp. 2073–2081, 2020.
DOI: https://doi.org/10.1109/JBHI.2020.2984724
H. Laufs, M. C. Walker, and T. E. Lund, “Brain activation and hypothalamic functional connectivity during human non-rapid eye movement sleep: an EEG/fMRI study—its limitations and an alternative approach,” Brain, vol. 130, no. 7, pp. e75–e75, 2007.
DOI: https://doi.org/10.1093/brain/awm126
A. Leino, H. Korkalainen, L. Kalevo, S. Nikkonen, S. Kainulainen, A. Ryan, B. Duce, K. Sipila, J. Ahlberg, J. Sahlman, T. Miettinen, S. Westeren-Punnonen, E. Mervaala, J. Toyräs, S. Myllymaa, T. Leppänen, and K. Myllymaa, “Deep learning enables accurate automatic sleep staging based on ambulatory forehead EEG,” IEEE Access, vol. 10, pp. 26554–26566, 2022.
DOI: https://doi.org/10.1109/ACCESS.2022.3179034
J. Liu, A. Asgarli, M. Banks, and K. Nourski, “Intracranial EEG reveals human cerebral cortical regions that are spared from alpha-delta sleep,” Sleep, vol. 47, Supplement 1, pp. A21–A22, 2024.
DOI: https://doi.org/10.1093/sleep/zsaa203.21
J. Lyu, W. Shi, C. Zhang, and C.-H. Yeh, “A novel sleep staging method based on EEG and ECG multimodal features combination,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 4073–4084, 2023
DOI: https://doi.org/10.1109/TNSRE.2023.3271215
S. Mousavi, F. Afghah, and U. R. Acharya, “SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach,” PloS One, vol. 14, no. 5, pp. e0216456, 2019.
DOI: https://doi.org/10.1371/journal.pone.0216456
Q. Xiong, X. Zhang, W.-F. Wang, and Y. Gu, "A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI," Computational and Mathematical Methods in Medicine, vol. 2020, Art. ID 9812019, 2020.
DOI: https://doi.org/10.1155/2020/9812019
A. Onargan, B. Gavcar, G. Caliskan, and A. Akan, “Prediction of sleep apnea using EEG signals and machine learning algorithms,” 2021 Medical Technologies Congress (TIPTEKNO), pp. 1–4, 2021.
DOI: https://doi.org/10.1109/TIPTEKNO53397.2021.9700270
S. Paisarnsrisomsuk, C. Ruiz, and S. Alvarez, “Improved deep learning classification of human sleep stages,” 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 338–343, 2020
DOI: https://doi.org/10.1109/CBMS49503.2020.00071
D. T. Pham and R. Moucek, “Automatic sleep stage classification by CNN-Transformer-LSTM using single-channel EEG signal,” 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2559–2563, 2023.
DOI: https://doi.org/10.1109/BIBM56479.2023.00393
M. A. Pinto-Orellana, H. Khan, H. Ombao, and P. Mirtaheri, “Emerging neuroimaging approach of hybrid EEG-fNIRS recordings: Data collection and analysis challenges,” Data Science in Science, vol. 3, no. 1, 2426785, 2024.
DOI: https://doi.org/10.1016/j.dssc.2024.2426785
] I. Pisarenco, M. Caporro, C. Prosperetti, and M. Manconi, “High-density electroencephalography as an innovative tool to explore sleep physiology and sleep related disorders,” International Journal of Psychophysiology, vol. 92, pp. 8–15, 2014.
DOI: https://doi.org/10.1016/j.ijpsycho.2013.12.006
W. Qu, Z. Wang, H. Hong, Z. Chi, D. D. Feng, R. Grunstein, and C. Gordon, “A residual based attention model for EEG-based sleep staging,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2833–2843, 2020.
DOI: https://doi.org/10.1109/JBHI.2020.2973172
A. Q. Rana, A. T. Ghouse, and R. Govindarajan, “Basics of electroencephalography (EEG),” Neurophysiology in Clinical Practice, pp. 3–9, 2017.
DOI: https://doi.org/10.1007/978-3-319-39342-1_1
J.-S. Sunwoo, K. S. Cha, J.-I. Byun, J.-S. Jun, T.-J. Kim, J.-W. Shin, S.-T. Lee, K.-H. Jung, K.-I. Park, K. Chu, et al., “Nonrapid eye movement sleep electroencephalographic oscillations in idiopathic rapid eye movement sleep behavior disorder: a study of sleep spindles and slow oscillations,” Sleep, vol. 44, no. 2, zsaa160, 2021.
DOI: https://doi.org/10.1093/sleep/zsaa160
G. Tan, Z. Liang, Y. Chi, Q. Li, B. Peng, Y. Liu, and J. Li, “Low-quality integrated circuits image verification based on low-rank subspace clustering with high-frequency texture components,” Applied Sciences, vol. 13, no. 155, pp. 1–12, 2022.
DOI: https://doi.org/10.3390/app13010155
U. Rajendra Acharya, O. Faust, N. Kannathal, T. L. Chua, and S. Laxminarayan, “Non-linear analysis of EEG signals at various sleep stages,” Computer Methods and Programs in Biomedicine, vol. 80, no. 1, pp. 37–45, 2005.
DOI: https://doi.org/10.1016/j.cmpb.2005.01.001
Y. W. Wong, H.-T. Cheng, S.-Q. Chen, and C.-Y. Yang, “Portable single-electrode EEG signal acquisition device for sleep disorder identification,” Proceedings of the 2024 International Conference on System Science and Engineering (ICSSE), pp. 1–6, 2024.
DOI: https://doi.org/10.1109/ICSSE61472.2024.10608873
H. Yao, J. H. Chen, and Y. F. Xu, “Patients with mental health disorders in the COVID-19 epidemic,” The Lancet Psychiatry, vol. 7, no. 4, pp. e21, 2020.
DOI: https://doi.org/10.1016/S2215-0366%2820%2930090-0
M. Younes, A. Azarbarzin, M. Reid, D. R. Mazzotti, and S. Redline, “Characteristics and reproducibility of novel sleep EEG biomarkers and their variation with sleep apnea and insomnia in a large community-based cohort,” Sleep, vol. 44, no. 10, pp. zsab145, 2021.
DOI: https://doi.org/10.1093/sleep%2Fzsab145
K. L. Lew, K. S. Sim, and Z. Ting, “Deep Learning Approach EEG Signal Classification,” International Journal on Informatics Visualization, vol. 8, no. 3–2, pp. 1693–1702, Nov. 2024.
DOI: http://dx.doi.org/10.62527/joiv.8.3-2.2959
H. Almutairi, G.M. Hassan and A. Datta, "Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review," Applied Sciences, vol. 13, no. 24, pp. 13280, 2023.
DOI: https://doi.org/10.3390/app132413280
M. Fu, Y. Wang, Z. Chen, J. Li, F. Xu, X. Liu, and F. Hou, “Deep learning in automatic sleep staging with a single-channel electroencephalography,” Frontiers in Physiology, vol. 12, pp. 628502, 2021.
DOI: https://doi.org/10.3389/fphys.2021.628502
K. Chua, Z.Y. Lim, K.S. Sim and S.C. Tan, "Development of Brain Balancing System for Left and Right Hemispheres," International Journal on Robotics, Automation and Sciences, vol. 3, pp. 1-7, 2021.
DOI: https://doi.org/10.33093/ijoras.2021.3.1
Z.Y. Lim, C.K. Toa, E. Rao and K.S. Sim, "Development of Augmented Reality Based Applications for Brain Memory Training," International Journal on Robotics, Automation and Sciences, vol. 5, no. 1, pp. 13-20, 2023.